mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-15 20:33:59 +08:00
Ethan Weber
fb440fedf3
* occupancy grid in progress * visualizing occupancy grid. currently restructing field and renderer head outputs * black and pylint. broke nerfw and semantic nerf but it's okay * get_project_root
267 lines
11 KiB
Python
267 lines
11 KiB
Python
"""
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Implementation of vanilla nerf.
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"""
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from typing import Dict, List, Tuple
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import torch
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from torch import nn
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from torch.nn import Parameter
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from torchmetrics import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure
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from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
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from mattport.nerf.field_modules.encoding import NeRFEncoding
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from mattport.nerf.field_modules.field_heads import DensityFieldHead, RGBFieldHead
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from mattport.nerf.field_modules.mlp import MLP
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from mattport.nerf.fields.base import Field
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from mattport.nerf.graph.base import Graph
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from mattport.nerf.loss import MSELoss
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from mattport.nerf.renderers import AccumulationRenderer, DepthRenderer, RGBRenderer
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from mattport.nerf.sampler import PDFSampler, UniformSampler
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from mattport.structures import colors
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from mattport.structures.rays import PointSamples, RayBundle
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from mattport.nerf.colliders import NearFarCollider
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from mattport.utils import stats_tracker, visualization, writer
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from mattport.utils.misc import is_not_none
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class NeRFField(Field):
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"""NeRF module"""
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OUTPUT_RGB = "rgb"
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def __init__(self, num_layers=8, layer_width=256, skip_connections: Tuple = (4,)) -> None:
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super().__init__()
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self.num_layers = num_layers
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self.layer_width = layer_width
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self.skip_connections = skip_connections
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self.build_encodings()
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self.build_mlp_base()
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self.build_mlp_rgb()
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self.build_heads()
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def build_encodings(self):
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"""Build the encodings."""
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self.encoding_xyz = NeRFEncoding(
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in_dim=3, num_frequencies=10, min_freq_exp=0.0, max_freq_exp=8.0, include_input=True
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)
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self.encoding_dir = NeRFEncoding(
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in_dim=3, num_frequencies=4, min_freq_exp=0.0, max_freq_exp=4.0, include_input=True
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)
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def build_mlp_base(self):
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"""Build the MLP base."""
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self.mlp_base = MLP(
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in_dim=self.encoding_xyz.get_out_dim(),
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out_dim=self.layer_width,
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num_layers=self.num_layers,
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layer_width=self.layer_width,
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skip_connections=self.skip_connections,
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activation=nn.ReLU(),
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)
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def build_mlp_rgb(self):
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"""Build the MLP for RGB."""
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self.mlp_rgb = MLP(
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in_dim=self.mlp_base.get_out_dim() + self.encoding_dir.get_out_dim(),
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out_dim=self.layer_width // 2,
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num_layers=2,
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layer_width=self.layer_width // 2,
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activation=nn.ReLU(),
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)
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def build_heads(self):
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"""Build the heads."""
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self.field_output_rgb = RGBFieldHead(in_dim=self.mlp_rgb.get_out_dim())
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self.field_output_density = DensityFieldHead(in_dim=self.mlp_base.get_out_dim())
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def get_density(self, point_samples: PointSamples, valid_mask=None):
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"""Computes and returns the densities."""
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positions = point_samples.positions
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if not is_not_none(valid_mask):
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valid_mask = torch.ones_like(positions[..., 0]).bool()
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# placeholders for values to return
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density = torch.zeros(*valid_mask.shape, 1, dtype=torch.float32, device=positions.device)
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base_mlp_out = torch.zeros(
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*valid_mask.shape, self.mlp_base.out_dim, dtype=torch.float32, device=positions.device
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)
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if not valid_mask.any(): # empty mask
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return density, base_mlp_out
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encoded_xyz = self.encoding_xyz(positions[valid_mask])
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base_mlp_out[valid_mask] = self.mlp_base(encoded_xyz)
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density[valid_mask] = self.field_output_density(base_mlp_out[valid_mask])
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return density, base_mlp_out
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def get_outputs(self, point_samples: PointSamples, density_embedding=None, valid_mask=None):
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directions = point_samples.directions
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if not is_not_none(valid_mask):
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valid_mask = torch.ones_like(directions[..., 0]).bool()
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# placeholders for values to return
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rgb = torch.zeros(*valid_mask.shape, 3, dtype=torch.float32, device=directions.device)
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if not valid_mask.any(): # empty mask
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return {"rgb": rgb}
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encoded_dir = self.encoding_dir(directions[valid_mask])
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rgb_mlp_out = self.mlp_rgb(torch.cat([encoded_dir, density_embedding[valid_mask]], dim=-1))
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rgb[valid_mask] = self.field_output_rgb(rgb_mlp_out)
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return {"rgb": rgb}
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class NeRFGraph(Graph):
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"""Vanilla NeRF graph"""
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def __init__(
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self,
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intrinsics=None,
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camera_to_world=None,
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near_plane=2.0,
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far_plane=6.0,
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num_coarse_samples=64,
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num_importance_samples=128,
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**kwargs,
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) -> None:
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self.near_plane = near_plane
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self.far_plane = far_plane
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self.num_coarse_samples = num_coarse_samples
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self.num_importance_samples = num_importance_samples
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self.field_coarse = None
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self.field_fine = None
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super().__init__(intrinsics=intrinsics, camera_to_world=camera_to_world, **kwargs)
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def populate_collider(self):
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self.collider = NearFarCollider(self.near_plane, self.far_plane)
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def populate_fields(self):
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"""Set the fields."""
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self.field_coarse = NeRFField()
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self.field_fine = NeRFField()
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def populate_misc_modules(self):
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# samplers
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self.sampler_uniform = UniformSampler(num_samples=self.num_coarse_samples)
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self.sampler_pdf = PDFSampler(num_samples=self.num_importance_samples)
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# renderers
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self.renderer_rgb = RGBRenderer(background_color=colors.WHITE)
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self.renderer_accumulation = AccumulationRenderer()
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self.renderer_depth = DepthRenderer()
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# losses
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self.rgb_loss = MSELoss()
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# metrics
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self.psnr = PeakSignalNoiseRatio(data_range=1.0)
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self.ssim = StructuralSimilarityIndexMeasure()
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self.lpips = LearnedPerceptualImagePatchSimilarity()
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def get_param_groups(self) -> Dict[str, List[Parameter]]:
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param_groups = {}
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param_groups["fields"] = list(self.field_coarse.parameters()) + list(self.field_fine.parameters())
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return param_groups
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def get_outputs(self, ray_bundle: RayBundle):
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# uniform sampling
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ray_samples_uniform = self.sampler_uniform(ray_bundle)
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# coarse field:
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field_outputs_coarse = self.field_coarse.forward(ray_samples_uniform.to_point_samples())
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weights_coarse = ray_samples_uniform.get_weights(field_outputs_coarse["density"])
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rgb_coarse = self.renderer_rgb(
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rgb=field_outputs_coarse["rgb"],
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weights=weights_coarse,
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)
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accumulation_coarse = self.renderer_accumulation(weights_coarse)
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depth_coarse = self.renderer_depth(weights_coarse, ray_samples_uniform.ts)
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# pdf sampling
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ray_samples_pdf = self.sampler_pdf(ray_samples_uniform, weights_coarse)
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# fine field:
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field_outputs_fine = self.field_fine.forward(ray_samples_pdf.to_point_samples())
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weights_fine = ray_samples_pdf.get_weights(field_outputs_fine["density"])
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rgb_fine = self.renderer_rgb(
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rgb=field_outputs_fine["rgb"],
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weights=weights_fine,
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)
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accumulation_fine = self.renderer_accumulation(weights_fine)
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depth_fine = self.renderer_depth(weights_fine, ray_samples_pdf.ts)
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# --- occupancy grid ---
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# TODO(ethan): move this elsewhere. currently here for debugging purposes
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density_occ = self.occupancy_grid.get_densities(ray_samples_pdf.positions)
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weights_occ = ray_samples_pdf.get_weights(density_occ)
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depth_occ = self.renderer_depth(weights_occ, ray_samples_pdf.ts)
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outputs = {
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"rgb_coarse": rgb_coarse,
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"rgb_fine": rgb_fine,
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"accumulation_coarse": accumulation_coarse,
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"accumulation_fine": accumulation_fine,
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"depth_coarse": depth_coarse,
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"depth_fine": depth_fine,
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"depth_occ": depth_occ,
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}
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return outputs
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def get_loss_dict(self, outputs, batch):
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device = outputs["rgb_coarse"].device
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pixels = batch["pixels"].to(device)
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rgb_loss_coarse = self.rgb_loss(pixels, outputs["rgb_coarse"])
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rgb_loss_fine = self.rgb_loss(pixels, outputs["rgb_fine"])
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loss_dict = {"rgb_loss_coarse": rgb_loss_coarse, "rgb_loss_fine": rgb_loss_fine}
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loss_dict["aggregated_loss"] = self.get_aggregated_loss_from_loss_dict(loss_dict)
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return loss_dict
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def log_test_image_outputs(self, image_idx, step, image, outputs):
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rgb_coarse = outputs["rgb_coarse"]
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rgb_fine = outputs["rgb_fine"]
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acc_coarse = visualization.apply_colormap(outputs["accumulation_coarse"])
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acc_fine = visualization.apply_colormap(outputs["accumulation_fine"])
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depth_coarse = visualization.apply_depth_colormap(
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outputs["depth_coarse"],
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accumulation=outputs["accumulation_coarse"],
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near_plane=self.near_plane,
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far_plane=self.far_plane,
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)
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depth_fine = visualization.apply_depth_colormap(
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outputs["depth_fine"],
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accumulation=outputs["accumulation_fine"],
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near_plane=self.near_plane,
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far_plane=self.far_plane,
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)
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combined_rgb = torch.cat([image, rgb_coarse, rgb_fine], dim=1)
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combined_acc = torch.cat([acc_coarse, acc_fine], dim=1)
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combined_depth = torch.cat([depth_coarse, depth_fine], dim=1)
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writer.write_image(name=f"image_idx_{image_idx}", image=combined_rgb, step=step, group="img")
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writer.write_image(name=f"image_idx_{image_idx}", image=combined_acc, step=step, group="accumulation")
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writer.write_image(name=f"image_idx_{image_idx}", image=combined_depth, step=step, group="depth")
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depth_occ = visualization.apply_depth_colormap(outputs["depth_occ"])
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writer.write_image(name=f"image_idx_{image_idx}", image=depth_occ, step=step, group="depth_occupancy_grid")
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# Switch images from [H, W, C] to [1, C, H, W] for metrics computations
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image = torch.moveaxis(image, -1, 0)[None, ...]
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rgb_coarse = torch.moveaxis(rgb_coarse, -1, 0)[None, ...]
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rgb_fine = torch.moveaxis(rgb_fine, -1, 0)[None, ...]
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coarse_psnr = self.psnr(image, rgb_coarse)
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fine_psnr = self.psnr(image, rgb_fine)
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fine_ssim = self.ssim(image, rgb_fine)
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fine_lpips = self.lpips(image, rgb_fine)
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writer.write_scalar(name=f"val_{image_idx}-coarse", scalar=float(coarse_psnr), step=step, group="psnr")
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writer.write_scalar(name=f"val_{image_idx}-fine", scalar=float(fine_psnr), step=step, group="psnr")
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writer.write_scalar(name=f"val_{image_idx}", scalar=float(fine_ssim), step=step, group="ssim")
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writer.write_scalar(name=f"val_{image_idx}", scalar=float(fine_lpips), step=step, group="lpips")
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stats_tracker.update_stats(
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{"name": stats_tracker.Stats.CURR_TEST_PSNR, "value": float(fine_psnr), "step": step}
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)
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return fine_psnr.item()
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